Improvement of training method for dynamic neural network

Kunihiko Nakazono, Kouhei Ohnishi, Hiroshi Kinjo, Tetsuhiko Yamamoto

研究成果: Conference contribution

抄録

In this research, we propose a dynamic neural network (DNN) with the characteristics of stiffness, viscosity, and inertia and a training algorithm based on the back-propagation (BP) method to include a momentum term. In a previous research, we proposed a training algorithm for the DNN based on the BP method or GA-based training method. However, in the previous method it was necessary to determine the values of the DNN parameters by trial and error. So, the modified BP method and GA-based training method were designed to train not only the connecting weights but also the property parameters of the DNN. We develop the BP method to include a momentum term in order to increase the convergence of the training effect. Simulation results show that the DNN with characteristics of stiffness, viscosity, and inertia trained by the modified BP method to include the momentum term obtains good training performances for time series signals generated from periodic function. In this paper, we compare the DNN with a conventional training method in order to verify the effectiveness of the DNN.

本文言語English
ホスト出版物のタイトルProceedings of the 12th International Symposium on Artificial Life and Robotics, AROB 12th'07
ページ324-327
ページ数4
出版ステータスPublished - 2007 12 1
イベント12th International Symposium on Artificial Life and Robotics, AROB 12th'07 - Oita, Japan
継続期間: 2007 1 252007 1 27

出版物シリーズ

名前Proceedings of the 12th International Symposium on Artificial Life and Robotis, AROB 12th'07

Other

Other12th International Symposium on Artificial Life and Robotics, AROB 12th'07
CountryJapan
CityOita
Period07/1/2507/1/27

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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